{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import io\n",
    "import os\n",
    "import platform\n",
    "import pdb\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from matplotlib.pyplot import cm\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from sklearn import preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def load_model(city, model, path='./'):\n",
    "    \n",
    "    # file name and path \n",
    "    filename = path + '{}.pt'.format(city)\n",
    "    \n",
    "    # load the model parameters \n",
    "    model.load_state_dict(torch.load(filename))\n",
    "    \n",
    "    \n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LSTM(nn.Module):\n",
    "    def __init__(self,input_size,hidden_size,num_layers=2,dropout=0,bidirectional=False):\n",
    "        super(LSTM,self).__init__()\n",
    "        self.input_size=input_size\n",
    "        self.hidden_size=hidden_size\n",
    "        self.num_layers=num_layers\n",
    "        self.dropout=dropout\n",
    "        self.bidirectional=bidirectional\n",
    "        self.lstm = nn.LSTM(input_size,\n",
    "                            hidden_size,\n",
    "                            num_layers,\n",
    "                            dropout=dropout,\n",
    "                            bidirectional=bidirectional)\n",
    "        self.linear = nn.Linear(hidden_size, input_size)\n",
    "        \n",
    "    def forward(self,inputs,hidden):\n",
    "        outputs,hidden=self.lstm(inputs,hidden)\n",
    "        predictions=self.linear(outputs[-1])\n",
    "        return predictions,outputs,hidden\n",
    "    \n",
    "    def init_hidden(self,batch_size):\n",
    "        num_directions=2 if self.bidirectional else 1\n",
    "        hidden = (torch.zeros(self.num_layers*num_directions, batch_size, self.hidden_size),\n",
    "                  torch.zeros(self.num_layers*num_directions, batch_size, self.hidden_size))\n",
    "        return hidden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "def init_para(city):\n",
    "    if city=='montreal':\n",
    "        input_size=13\n",
    "        pm25_index=-1\n",
    "    elif city=='vancouver':\n",
    "        input_size=16\n",
    "        pm25_index=-3\n",
    "    elif city=='toronto' or city=='ottawa':\n",
    "        input_size=13\n",
    "        pm25_index=-1\n",
    "    else:\n",
    "        input_size=1\n",
    "        pm25_index=0\n",
    "    return input_size,pm25_index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "city='toronto'\n",
    "hidden_size=24\n",
    "input_size,pm25_index=init_para(city)\n",
    "model=LSTM(input_size,hidden_size)\n",
    "model=load_model(city,model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.LSTM"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_data(city):\n",
    "    dataset=pd.read_csv(city+'.csv')\n",
    "    if city=='vancouver':\n",
    "        dataset=dataset.iloc[:,2:].copy()\n",
    "    elif city=='beijing' or city=='chengdu' or city=='shanghai' or city=='shenyang':\n",
    "        dataset=dataset.iloc[:,-1].copy()\n",
    "    dataset=dataset.astype(np.float32)\n",
    "    dataset.replace(9999,np.nan,inplace=True)\n",
    "    dataset.replace(-999,np.nan,inplace=True)\n",
    "    for i in range(len(dataset)):\n",
    "        if city=='beijing' or city=='chengdu' or city=='shanghai' or city=='shenyang':\n",
    "            if np.isnan(dataset.iat[i]):\n",
    "                if i==0:\n",
    "                    dataset.iloc[i]=dataset.iat[i+1]\n",
    "                elif i==len(dataset)-1:\n",
    "                    dataset.iloc[i]=dataset.iat[i-1]\n",
    "                else:\n",
    "                    dataset.iloc[i]=np.nanmean([dataset.iat[i-1],dataset.iat[i+1]])\n",
    "            \n",
    "        else:\n",
    "            for j in range(len(dataset.columns)):\n",
    "                if np.isnan(dataset.iat[i,j]):\n",
    "                    if i==0:\n",
    "                        dataset.iloc[i,j]=dataset.iat[i+1,j]\n",
    "                    elif i==len(dataset)-1:\n",
    "                        dataset.iloc[i,j]=dataset.iat[i-1,j]\n",
    "                    else:\n",
    "                        dataset.iloc[i,j]=np.nanmean([dataset.iat[i-1,j],dataset.iat[i+1,j]])\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset=read_data(city)\n",
    "split=round(0.90*len(dataset))\n",
    "data1=dataset.iloc[:split].copy()\n",
    "test_set=dataset.iloc[split:].copy()\n",
    "scaler = preprocessing.MinMaxScaler() \n",
    "test_set1 = scaler.fit_transform(test_set)\n",
    "# test_set1=scaler.transform(test_set)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "seq_len=30 + 1\n",
    "x=len(data1)-seq_len\n",
    "y=len(test_set)-seq_len\n",
    "# sequences = [np.asarray(data1[t:t+seq_len]) for t in range(x)]\n",
    "test_seq=[np.asarray(test_set[t:t+seq_len]) for t in range(y)]\n",
    "test_seq1=[np.asarray(test_set1[t:t+seq_len]) for t in range(y)]\n",
    "# seq=torch.FloatTensor(np.asarray(sequences))\n",
    "test_seq=torch.FloatTensor(np.asarray(test_seq))\n",
    "test_seq1=torch.FloatTensor(test_seq1)\n",
    "# split_row=round(0.80*seq.size(0))\n",
    "# x_train_set=seq[:split_row, :-1]\n",
    "# y_train_set=seq[:split_row, -1:]\n",
    "# x_valid_set=seq[split_row:, :-1]\n",
    "# y_valid_set=seq[split_row:, -1:]\n",
    "x_test_set=test_seq1[:,:-1]\n",
    "y_test_set=test_seq[:,-1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data=np.asarray(dataset,dtype='float32')\n",
    "# seq_len = 30 + 1\n",
    "# x=len(data)-seq_len\n",
    "# sequences = [data[t:t+seq_len] for t in range(x)]\n",
    "# print(type(sequences))\n",
    "# sequences=torch.FloatTensor(np.asarray(sequences))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for i in range(len(sequences)):\n",
    "#     sequences[i]=np.asarray(sequences[i])\n",
    "# sequences=torch.FloatTensor(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# input_set=np.asarray(scaled_values,dtype=np.float32)\n",
    "# seq_len=30+1\n",
    "# x=len(input_set)-seq_len\n",
    "# sequences=[input_set[t:t+seq_len] for t in range(x)]\n",
    "# min_max=[]\n",
    "# for i in range(len(sequences)):\n",
    "#     temp=[]\n",
    "#     temp.append(sequences[i].min())\n",
    "#     temp.append(sequences[i].max())\n",
    "#     min_max.append(temp)\n",
    "#     sequences.append(scaler.fit_transform(sequences[i]))\n",
    "# x_set=torch.FloatTensor(sequences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7853"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([845, 1, 13])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_set.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# min_max_valid[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "def predict_full_sequence(model, x, input_size, num_steps,pm25_index):\n",
    "    \"\"\"Predicts one-step ahead with a single sequence from the test set\n",
    "       and use predictions to predict the remaining test set sequence\n",
    "       for num_steps.\n",
    "    \"\"\"\n",
    "    predictions = torch.zeros(num_steps)\n",
    "    hidden = model.init_hidden(1)\n",
    "#     y_pred, _, hidden = model(x.contiguous().view(-1, 1, input_size), hidden)\n",
    "#     x = torch.cat((x, y_pred))\n",
    "#     predictions[0] = y_pred[:,pm25_index]\n",
    "    for i in range(0, num_steps):\n",
    "        y_pred, _, hidden = model(x.contiguous().view(-1, 1, input_size), hidden)\n",
    "#         x = x*(min_max_valid[i,1]-min_max_valid[i,0])+min_max_valid[i,0]\n",
    "        x = torch.cat((x, y_pred))\n",
    "        x = x[1:]\n",
    "        predictions[i] = torch.FloatTensor(scaler.inverse_transform(np.expand_dims(y_pred[0].data, axis=0))[:,-1])\n",
    "#         predictions[i]=y_pred[:,pm25_index]\n",
    "    return predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "index=100\n",
    "x = torch.FloatTensor(x_test_set[index])\n",
    "# size=x_valid_set.size(0)\n",
    "# print(y_valid_set)\n",
    "num_steps = 24  # Do not set to higher value due to memory constraint\n",
    "full_predictions = predict_full_sequence(model, x, input_size, num_steps,pm25_index)\n",
    "fig = plt.figure(figsize=(14, 7))\n",
    "# plt.plot(range(0, x.size(0)), x[:,pm25_index].data.numpy(), color=\"royalblue\", label=\"Inputs\")\n",
    "y=y_test_set.squeeze(1)[index:index+num_steps,pm25_index]\n",
    "plt.plot(y.data.numpy(),color='darkcyan')\n",
    "plt.plot(full_predictions.data.numpy())\n",
    "\n",
    "# plt.plot(range(x.size(0), x.size(0)+num_steps), y_valid_set[:,pm25_index].data.numpy(), color=\"darkcyan\", label=\"True\")\n",
    "# plt.plot(range(x.size(0), x.size(0)+num_steps), full_predictions.data.numpy(),\n",
    "#             color=\"tomato\", label=\"Predictions\")\n",
    "# plt.legend(loc=\"upper right\", fontsize=14)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor([ 9.2646,  8.5441,  7.8071,  7.1583,  6.6569,  6.3403,  6.2225,\n",
      "         6.2909,  6.5109,  6.8333,  7.2044,  7.5749,  7.9061,  8.1730,\n",
      "         8.3647,  8.4821,  8.5350,  8.5384,  8.5089,  8.4621,  8.4114,\n",
      "         8.3673,  8.3377,  8.3283])\n"
     ]
    }
   ],
   "source": [
    "print(full_predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([24, 13])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "def eval_loss(model,input_size,pm25_index,num_steps,y_valid_set,x_valid_set,loss_fn):\n",
    "    total_loss=0\n",
    "    losses=[]\n",
    "    for i in range(y_valid_set.size(0)-num_steps):\n",
    "        index=i\n",
    "        x = torch.FloatTensor(x_valid_set[index])\n",
    "        # size=x_valid_set.size(0)\n",
    "        # print(y_valid_set)\n",
    "        full_predictions = predict_full_sequence(model, x, input_size, num_steps,pm25_index)\n",
    "        y=y_valid_set.squeeze(1)[index:index+num_steps,pm25_index]\n",
    "        loss=loss_fn(full_predictions,y)\n",
    "        total_loss+=torch.sqrt(loss)\n",
    "        losses.append(torch.sqrt(loss))\n",
    "    return total_loss/y_valid_set.size(0),losses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tensor(4.2085)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "num_steps=24\n",
    "loss_fn=nn.MSELoss()\n",
    "loss,losses=eval_loss(model,input_size,pm25_index,num_steps,y_test_set,x_test_set,loss_fn)\n",
    "print(loss)\n",
    "fig = plt.figure(figsize=(14, 7))\n",
    "plt.plot(losses)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 7))\n",
    "plt.plot(losses[:])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
